Function mining based on gene Expression Programming and Particle Swarm Optimization

2009 ◽  
Author(s):  
Taiyong Li ◽  
Tiangang Dong ◽  
Jiang Wu ◽  
Ting He
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Gene Expression Programming ◽  
Particle Swarm ◽  
Swarm Optimization

Algorithm of Coal Mine Rescue Robot Model Based on PSO and GEP

2013 ◽  
Vol 416-417 ◽  
pp. 739-742
Author(s):  
Xue Chen Wang ◽  
Xiao Guang Yue
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Coal Mine ◽  
Gene Expression Programming ◽  
Particle Swarm ◽  
Programming Algorithm ◽  
Swarm Optimization ◽  
Rescue Robot ◽  
Model Based ◽  
Mine Rescue

In order to study a mine rescue robot model, gene expression programming algorithm is studied. The gene expression programming Algorithm can simulate many scientific models, and has been successfully applied in many aspects. Particle swarm optimization algorithm is discussed. Each member of the particle swarm optimization group can study its own experience and other members' experience to continuously change their search mode. Finally, a coal mine rescue robot model based on the gene expression programming and particle swarm optimization is put forward.

Shear strength prediction of short circular reinforced-concrete columns using soft computing methods

2020 ◽  
Vol 23 (14) ◽  
pp. 3048-3061
Author(s):  
Hesam Ketabdari ◽  
Farzad Karimi ◽  
Mahsa Rasouli
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Shear Strength ◽  
Particle Swarm Optimization ◽  
Reinforced Concrete ◽  
Gene Expression Programming ◽  
Particle Swarm ◽  
Concrete Columns ◽  
Swarm Optimization ◽  
Reinforced Concrete Columns

In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affecting the shear strength include axial force, longitudinal and transverse reinforcement, column dimension ratio, concrete compressive strength and ductility. In this respect, first, 200 numerical models of the short circular reinforced-concrete column incorporating various effective parameters so that a sufficient number of outputs could be provided, are analyzed by ABAQUS software to compute their shear strengths. Then, the gene expression programming and particle swarm optimization algorithms are employed to predict the shear strengths and by means of each algorithm, a relation was proposed accordingly. Then, using the experimental data, these relations are evaluated by comparing with those specified in ACI 318 and ASCE-ACI 426. The results indicate that the percentage of relative error between the experimental data and the values obtained from ACI 318 and ASCE-ACI 426 is respectively equal to 25% and 30%, which have been reduced to 13% and 9% through the gene expression programming and particle swarm optimization algorithms implying the satisfactory performance of these two algorithms. Finally, a comparison of the gene expression programming and particle swarm optimization is investigated in terms of convergence rate, degree of accuracy, and performance mechanism.

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

2020 ◽  
Vol 10 (5) ◽  
pp. 1049-1056
Author(s):  
M. Kalaiarasu ◽  
J. Anitha
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Expression Data ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Weighted Clustering

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

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